Search (71 results, page 1 of 4)

  • × year_i:[2020 TO 2030}
  • × theme_ss:"Computerlinguistik"
  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.11 
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    Abstract
    This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
    Type
    a
  2. Barthel, J.; Ciesielski, R.: Regeln zu ChatGPT an Unis oft unklar : KI in der Bildung (2023) 0.00 
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    Date
    29. 3.2023 13:23:26
    29. 3.2023 13:29:19
    Type
    a
  3. Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023) 0.00 
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    Abstract
    Das Forschungsunternehmen OpenAI hat ein neues Sprachmodell vorgestellt: ChatGPT. Es hat einen regelrechten Hype ausgelöst: Viele Nutzer haben Beispiele in den sozialen Medien gepostet, die die vielfältigen Fähigkeiten demonstrieren. Das darf aber nicht darüber hinwegtäuschen, dass auch ChatGPT einige gefährliche Schwächen hat, die für Sprachmodelle typisch sind.
    Date
    29.12.2022 18:22:55
    Type
    a
  4. Stieler, W.: Anzeichen von Bewusstsein bei ChatGPT und Co.? (2023) 0.00 
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    Abstract
    Ein interdisziplinäres Forschungsteam hat eine Liste von Eigenschaften aufgestellt, die auf Bewusstsein deuten, und aktuelle KI-Systeme darauf abgeklopft. Ein interdisziplinäres Forscherteam hat ein Paper [https://arxiv.org/abs/2308.08708] veröffentlicht, das eine Liste von 14 "Indikatoren" für Bewusstsein enthält, die aus sechs aktuellen Theorien über das Bewusstsein stammen. Aktuelle KI-Modelle wie GPT-3, Palm-E oder AdA von Deepmind weisen demnach einzelne dieser Indikatoren auf. "Es spricht viel dafür, dass die meisten oder alle Bedingungen für das Bewusstsein, die von derzeitigen Theorien vorgeschlagenen Bedingungen für das Bewusstsein mit den bestehenden Techniken der KI erfüllt werden können", schreiben die Autoren. Zum Team gehörte auch der Deep-Learning-Pionier Yoshua Bengio von der Université de Montréal.
    Type
    a
  5. Leighton, T.: ChatGPT und Künstliche Intelligenz : Utopie oder Dystopie? (2023) 0.00 
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    Series
    Telepolis / Kultur und Medien
    Type
    a
  6. Schneider, R.U.: Darf der Computer die Seminararbeit schreiben? (2023) 0.00 
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    Source
    Neue Zürcher Zeitung, 2023, vom 22.11.2023
    Type
    a
  7. Müller, P.: Text-Automat mit Tücken (2023) 0.00 
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    Source
    Pirmasenser Zeitung. Nr. 29 vom 03.02.2023, S.2
    Type
    a
  8. Lobo, S.: ¬Das Ende von Google, wie wir es kannten : Bessere Treffer durch ChatGPT (2022) 0.00 
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    Abstract
    Höchste Alarmstufe bei der weltgrößten Suchmaschine: Mit ChatGPT und künstlicher Intelligenz könnte eine neue Ära beginnen.
    Source
    https://www.spiegel.de/netzwelt/netzpolitik/bessere-treffer-durch-chatgpt-das-ende-von-google-wie-wir-es-kannten-kolumne-a-77820af6-51d7-4c03-b822-cf93094fd709
    Type
    a
  9. Hartnett, K.: Sind Sprachmodelle bald die besseren Mathematiker? (2023) 0.00 
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    Abstract
    Algorithmen wie ChatGPT tun sich selbst mit einfachen Rechenaufgaben noch immer schwer. Zwei neue Ansätze sollen das ändern - und könnten KI deutlich intelligenter machen.
    Type
    a
  10. Bischoff, M.: Was steckt hinter ChatGTP & Co? (2023) 0.00 
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    Date
    12. 4.2023 20:29:54
    Type
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  11. Dampz, N.: ChatGPT interpretiert jetzt auch Bilder : Neue Version (2023) 0.00 
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    Abstract
    Das kalifornische Unternehmen Open AI hat eine neue Version ihres Chatbots ChatGPT vorgestellt. Auffallendste Neuerung: Die Software, die mit Künstlicher Intelligenz funktioniert und bisher auf Text ausgerichtet war, interpretiert nun auch Bilder.
  12. Räwel, J.: Automatisierte Kommunikation (2023) 0.00 
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    Telepolis / Kultur und Medien
    Type
    a
  13. Lund, B.D.; Wang, T.; Mannuru, N.R.; Nie, B.; Shimray, S.; Wang, Z.: ChatGPT and a new academic reality : artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing (2023) 0.00 
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    Abstract
    This article discusses OpenAI's ChatGPT, a generative pre-trained transformer, which uses natural language processing to fulfill text-based user requests (i.e., a "chatbot"). The history and principles behind ChatGPT and similar models are discussed. This technology is then discussed in relation to its potential impact on academia and scholarly research and publishing. ChatGPT is seen as a potential model for the automated preparation of essays and other types of scholarly manuscripts. Potential ethical issues that could arise with the emergence of large language models like GPT-3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing.
    Date
    19. 4.2023 19:29:44
    Type
    a
  14. Albrecht, I.: GPT-3: die Zukunft studentischer Hausarbeiten oder eine Bedrohung der wissenschaftlichen Integrität? (2023) 0.00 
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    Date
    28. 1.2022 11:05:29
    Type
    a
  15. Pepper, S.; Arnaud, P.J.L.: Absolutely PHAB : toward a general model of associative relations (2020) 0.00 
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    Abstract
    There have been many attempts at classifying the semantic modification relations (R) of N + N compounds but this work has not led to the acceptance of a definitive scheme, so that devising a reusable classification is a worthwhile aim. The scope of this undertaking is extended to other binominal lexemes, i.e. units that contain two thing-morphemes without explicitly stating R, like prepositional units, N + relational adjective units, etc. The 25-relation taxonomy of Bourque (2014) was tested against over 15,000 binominal lexemes from 106 languages and extended to a 29-relation scheme ("Bourque2") through the introduction of two new reversible relations. Bourque2 is then mapped onto Hatcher's (1960) four-relation scheme (extended by the addition of a fifth relation, similarity , as "Hatcher2"). This results in a two-tier system usable at different degrees of granularities. On account of its semantic proximity to compounding, metonymy is then taken into account, following Janda's (2011) suggestion that it plays a role in word formation; Peirsman and Geeraerts' (2006) inventory of 23 metonymic patterns is mapped onto Bourque2, confirming the identity of metonymic and binominal modification relations. Finally, Blank's (2003) and Koch's (2001) work on lexical semantics justifies the addition to the scheme of a third, superordinate level which comprises the three Aristotelean principles of similarity, contiguity and contrast.
    Type
    a
  16. Laparra, E.; Binford-Walsh, A.; Emerson, K.; Miller, M.L.; López-Hoffman, L.; Currim, F.; Bethard, S.: Addressing structural hurdles for metadata extraction from environmental impact statements (2023) 0.00 
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    Abstract
    Natural language processing techniques can be used to analyze the linguistic content of a document to extract missing pieces of metadata. However, accurate metadata extraction may not depend solely on the linguistics, but also on structural problems such as extremely large documents, unordered multi-file documents, and inconsistency in manually labeled metadata. In this work, we start from two standard machine learning solutions to extract pieces of metadata from Environmental Impact Statements, environmental policy documents that are regularly produced under the US National Environmental Policy Act of 1969. We present a series of experiments where we evaluate how these standard approaches are affected by different issues derived from real-world data. We find that metadata extraction can be strongly influenced by nonlinguistic factors such as document length and volume ordering and that the standard machine learning solutions often do not scale well to long documents. We demonstrate how such solutions can be better adapted to these scenarios, and conclude with suggestions for other NLP practitioners cataloging large document collections.
    Date
    29. 8.2023 19:21:01
    Type
    a
  17. Hahn, U.: Automatische Sprachverarbeitung (2023) 0.00 
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  18. ¬Der Student aus dem Computer (2023) 0.00 
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    Date
    27. 1.2023 16:22:55
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  19. Thomas, I.S.; Wang, J.; GPT-3: Was euch zu Menschen macht : Antworten einer künstlichen Intelligenz auf die großen Fragen des Lebens (2022) 0.00 
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    Date
    7. 1.2023 18:41:29
  20. Donath, A.: Nutzungsverbote für ChatGPT (2023) 0.00 
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    Content
    Milliardenbewertung für ChatGPT OpenAI, das Chatbot ChatGPT betreibt, befindet sich laut einem Bericht des Wall Street Journals in Gesprächen zu einem Aktienverkauf. Das WSJ meldete, der mögliche Verkauf der Aktien würde die Bewertung von OpenAI auf 29 Milliarden US-Dollar anheben. Sorgen auch in Brandenburg Der brandenburgische SPD-Abgeordnete Erik Stohn stellte mit Hilfe von ChatGPT eine Kleine Anfrage an den Brandenburger Landtag, in der er fragte, wie die Landesregierung sicherstelle, dass Studierende bei maschinell erstellten Texten gerecht beurteilt und benotet würden. Er fragte auch nach Maßnahmen, die ergriffen worden seien, um sicherzustellen, dass maschinell erstellte Texte nicht in betrügerischer Weise von Studierenden bei der Bewertung von Studienleistungen verwendet werden könnten.

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